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Apache Airflow

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Workflow orchestration for data engineering pipelines

Paid plans only, free trial
Paid Workflow Automation ETL & Data Pipelines DevOps
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Reviews on G2
129 reviews tracked 1 press mention

The Bottom Line

Price
Paid plans only, free trial. Compare plans

Key facts

  • Apache Airflow is a workflow orchestration platform for programmatically authoring and scheduling data pipelines
  • It defines workflows as code using Python DAGs, with built-in monitoring and retry capabilities
  • Free and open-source, with managed versions available

Pros

  • Best workflow orchestration
  • Python-based DAGs
  • Large community
  • Many operators
  • Good monitoring

Cons

  • Resource intensive
  • Complex setup
  • Learning curve
  • Debugging difficult
  • Scaling needs work

What is Apache Airflow?

Editorial review
Airflow orchestrates complex data pipelines by letting you define workflows as code. Instead of scheduling scripts with cron and hoping they run in order, you write Python that describes dependencies between tasks. The scheduler handles retries, parallelism, and backfills automatically. A web interface shows what's running, what failed, and where data is in your pipeline. Alerts notify you when things go wrong. Data engineering teams treat Airflow as essential infrastructure. When pipelines grow from a few scripts to hundreds of interconnected jobs, Airflow provides the structure that keeps everything manageable.

Available on: Web

how we evaluate Source airflow.apache.org ↗

Ratings Across the Web

4.4 (129 reviews)

Apache Airflow holds an aggregate rating of 4.4 out of 5 from 129 reviews across G2, last checked September 17, 2026.

G2 129 reviews
4.4/5

Ratings aggregated from independent review platforms. Learn more

Key Features

  • Workflow orchestration
  • DAGs
  • Scheduling
  • Monitoring
  • Operators
  • Open source

Pricing Plans

Free Trial

Pricing checked Oct 1, 2026

Apache Airflow plans and prices, checked October 2026
PlanPriceDetails
Open Source

Custom

Usage-based pricing

  • Full functionality
  • Community support
  • Self-managed infrastructure
+1 more
  • Apache license
AWS MWAA Popular

$0.49/hour

Managed service

  • Fully managed
  • Auto-scaling
  • AWS integration
+1 more
  • Pay-as-you-go
Astronomer

$0.35/hour

Managed platform

  • Expert support
  • Easy deployment
  • Monitoring included
+1 more
  • Team features
Calculate your cost View full pricing

Is Apache Airflow worth the price?

Great value

The pricing for Apache Airflow is incredibly fair and generous, especially considering the robust open-source option.

AWS MWAA at $0.49/hour and Astronomer at $0.35/hour offer competitive managed solutions, providing excellent value for those needing less operational overhead. This pricing model is best for a wide range of users, from small startups to large enterprises, depending on their operational capacity and support needs.

Hidden Costs & Gotchas

AWS MWAA data transfer and storage fees

Astronomer potential add-on features

Full Apache Airflow pricing: every plan, hidden costs and alternatives

Reviews

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4.4/5

Across 129 verified user reviews on G2

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Best Apache Airflow Alternatives

Top alternatives based on features, pricing, and user needs.

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Prefect Freemium

Modern workflow orchestration platform

4.5
Meltano logo
Meltano Free

Open-source data integration platform

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Dagster logo
Dagster Paid

Data orchestration platform for ML pipelines

See all workflow automation tools →

Still deciding?

Most buyers shortlist 2 or 3 tools before committing. Pull a side-by-side comparison or browse the full alternatives shortlist below.

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Apache Airflow FAQ

How does Apache Airflow help with data engineering pipelines?

Apache Airflow orchestrates complex data pipelines by allowing users to define workflows as code. It manages task dependencies, schedules execution, and automates retries, parallelism, and backfills, ensuring pipelines run reliably.

Which teams benefit most from using Apache Airflow?

Data engineering teams find Apache Airflow essential infrastructure, especially when managing pipelines that scale from a few scripts to hundreds of interconnected jobs. It provides the structure needed to keep complex data workflows manageable.

What kind of limitations should users consider with Apache Airflow?

Users should be aware that Apache Airflow can be resource-intensive and has a complex setup process. It also presents a learning curve, and debugging issues within pipelines can be challenging.

How does Apache Airflow compare to Dagster for workflow orchestration?

Apache Airflow is recognized for its robust workflow orchestration capabilities and Python-based DAGs, supported by a large community and many operators. Dagster is another tool in the same category, offering similar workflow definition and execution features.

Does Apache Airflow include a free tier?

Apache Airflow does not include a permanently free tier. It is offered as a paid product, requiring users to invest in its implementation and maintenance.

Can Apache Airflow monitor pipeline execution and provide alerts?

Yes, Apache Airflow includes a web interface that allows users to monitor what is running, what has failed, and the current status of data within their pipelines. It also provides alerts to notify users when issues arise.

Source: airflow.apache.org

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